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How to Reduce LLM Token Costs by 85%

A practical guide to semantic compression — how it works, when to use it, and how to integrate it into your AI workflow without sacrificing output quality.

James Hollingsworth(Contributor)Published 8 min~358 words

The Token Cost Problem

Every LLM API call costs money. GPT-4, Claude, and Gemini all charge per token, and context windows are getting larger, not cheaper. A typical coding agent session can burn through 100K+ tokens per task.

The math is simple: if you can compress your context by up to 85% without losing meaning, you save up to 85% on token costs.

What is Semantic Compression?

Semantic compression goes beyond simple text truncation. Instead of cutting text at an arbitrary character limit, it:

  • Parses the document structure: headings, paragraphs, code blocks, lists
  • Builds a semantic graph: maps relationships between concepts
  • Ranks by importance: uses PageRank-style algorithms on the semantic graph
  • Preserves key information: keeps the skeleton that carries meaning
  • Removes redundancy: eliminates repeated concepts and filler
  • The result reads naturally and preserves the information an LLM needs to produce high-quality outputs.

    Getting Started

    1. Create an account

    Sign up at gotcontext.ai. The free tier includes 1,000 compressions/month.

    2. Generate an API key

    Go to your dashboard settings and create a new API key.

    3. Connect via MCP

    Add to your Claude Code config:

    ``json { "mcpServers": { "gotcontext": { "url": "https://api.gotcontext.ai/mcp", "headers": { "Authorization": "Bearer gc_live_YOUR_API_KEY" } } } } `

    4. Start saving

    Your AI tool will now automatically have access to compression tools. Add a note to your CLAUDE.md:

    ` When context is large (>10K tokens), use gotcontext's ingest_context tool to compress before processing. ``

    Real-World Results

    Document TypeOriginalCompressedSavings
    API documentation7,200 tokens1,440 tokens80%
    Source code (500 lines)4,200 tokens1,260 tokens70%
    Large codebase (50 files)48,000 tokens7,200 tokens85%

    When to Compress

    Compression works best for:

  • Large context windows: documentation, codebases, chat histories
  • Repeated context: the same background info sent with every prompt
  • Retrieval augmented generation: compress retrieved chunks before injection
  • It's less useful for:

  • Very short texts (< 100 tokens)
  • Highly structured data (JSON, CSV); these are already compact
  • Content where every word matters (legal contracts, poetry)
  • Pricing

  • Free: 1,000 compressions/month, 17 core MCP tools
  • Pro ($49/mo): 50,000 compressions/month, all 100+ MCP tools
  • Team ($99/mo): 100,000 compressions/month pooled, RBAC, batch queue
  • Business ($199/mo): Self-hosted Docker, OIDC, audit-log export, SOC2
  • Enterprise Dedicated ($499/mo): Reserved-capacity pool, 99.9% SLA
  • Get started free →

    Cite this

    Researchers, analysts, or journalists referencing this post can use either format below — both are copyable.

    BibTeXbibtex
    @misc{reduce-llm-token-costs-2026,
      title  = {How to Reduce LLM Token Costs by 85%},
      author = {James Hollingsworth},
      year   = {2026},
      month  = {April},
      url    = {https://www.gotcontext.ai/blog/reduce-llm-token-costs},
      note   = {gotcontext.ai engineering blog.},
    }
    APAtext
    James Hollingsworth. (2026, April 14). How to Reduce LLM Token Costs by 85%. gotcontext.ai. Retrieved from https://www.gotcontext.ai/blog/reduce-llm-token-costs.

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